code: def test_simple_multi_input_conv_intermediate(self) -> None:
      net = BasicModel_ConvNet_One_Conv(inplace=True)
      inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
      inp2 = torch.ones((1, 1, 4, 4))
      self._ablation_test_assert(
          net,
          net.relu1,
          (inp, inp2),
          [[[[4.0, 13.0], [40.0, 49.0]], [[0, 0], [-15.0, -24.0]]]],
          perturbations_per_eval=(1, 2, 4, 8, 12, 16),
      )
      self._ablation_test_assert(
          net,
          net.relu1,
          (inp, inp2),
          ([[[4.0, 13.0], [40.0, 49.0]], [[0, 0], [-15.0, -24.0]]],),
          baselines=torch.tensor(
              [[[-4.0, -13.0], [-2.0, -2.0]], [[0, 0], [0.0, 0.0]]]
          ),
          perturbations_per_eval=(1, 2, 4, 8, 12, 16),
          attribute_to_layer_input=True,
      )
      self._ablation_test_assert(
          net,
          net.relu1,
          (inp, inp2),
          [[[[17.0, 17.0], [67.0, 67.0]], [[0, 0], [-39.0, -39.0]]]],
          perturbations_per_eval=(1, 2, 4),
          layer_mask=torch.tensor(
              [[[[0, 0], [1, 1]], [[2, 2], [3, 3]]]]
          ),
      )
  
file_path: captum/tests/attr/layer/test_layer_ablation.py
imports: import unittest
  from typing import Any, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType
  from captum.attr._core.layer.layer_feature_ablation import LayerFeatureAblation
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 4 for calling the Test obj's underlying member method code with example usage: obj.test_simple_multi_input_conv_intermediate() without return types